Politicians and economists promise higher standards of living. Are there reliable, objective measures to help determine when theirpolicies are achieving this goal?

　　At present, most analyses of the question draw primarily on aggregate numbers computed regularly in every country, offered with little commentary on their reliability, either conceptual or in measurement. The numbers draw on a roughly century-old academic field of computing national statistics and a field of study called “macroeconomics.” Economists largely initiated this global record-keeping. However, the various government departments and international institutions that compute the numbers rarely comment on key factors that are highly relevant to understanding their significance.

　　As economists have sharply and repeatedly pointed out, these statistics can mean very different things in countries with morecentralized or decentralized decision-making. It is highly misleading to compare statistics originating from centralized countries to decentralized ones. This is especially important becausegovernments can usethese national aggregate statistics and models to rationalize bad policy, raising false hopes for improvements in living standards that are unlikely to materialize.

　　In many ways, the entire “macroeconomic sector” is comparable to the astrology that guided people’s decisions centuries ago. In a future essay, I will discuss alternative data—objective ones, alluded in this essay’s very last paragraph—that governments can rely upon if their goal is to raise standards of living, rather than staying in power and controlling the economy. Perhaps the timing is right, and the DOGE can start getting rid of macro-mythology and institutions based on it. That would be a huge win for good government, and for human prosperity more generally.

　　Two Historical Precedents

　　History shows no clear correlation between real prosperity and the keeping of macroeconomic statistics.For a start, two examples will suffice to illustrate the point. Let us compare Hong Kong and Argentina.

　　John Cowperthwaite, who became Hong Kong’s financial secretary in 1961, carried out a spectacularly successful policy raising standards of living—never replicated—refusing to compute any but the most rudimentary statistics. In 1961, Hong Kong residents earned on average of 25 percent of their British counterparts. By 1990, they leapfrogged the Brits. Except for its port, Hong Kong had neither “natural resources” nor agriculture. However, its fiscal policy—having the closest approximation to a flat tax, highest marginal rate being 18 percent—and its monetary one—having since 1983 a currency board linking its currency to the US dollar—brought both brains and social mobility to Hong Kong, resulting in the city’s economic “miracle.”

　　Cowperthwaite achieved all of this even while refusing to have any department computing “national statistics.” When a “macroeconomic” Whitehall delegation became alarmed that the Hong Kong government did not collect aggregate statistics, employment-related in particular, and refused to raise taxes, he sent the delegation promptly back to England. His argument was simple: such statistics only create political pressure groups to tax more, redistribute, and (mis)manage the economy. Hong Kong is just one example of an economy that succeeded spectacularly in distant pasts and in different countries without relying on using national aggregates and “macroeconomics,” as the numbers were not yet computed and “macroeconomics” did not exist.

　　Argentina, much in the news of late, carried out drastic policy changes, discarding reliance on statistics that for decades had the credibility of magic rabbits pulled from hats. Even the IMF stopped publishing the country’s inflation data between 2010 and 2014. The re-start of publishing estimates in 2014 did nothing to improve Argentina’s policies and standards of living. By 2019, the inflation rate stood at 80 percent, and in 2024 at about 300 percent. Publishing these and other aggregate numbers (whose reliability was nil) did nothing to help Argentinians put their house in order, though it did keep armies of bureaucrats, economists, and statisticians employed. Mr. Javier Milei, Argentina’s president since 2023, has been drastically decentralizing policies, which as of now seems to be succeeding.

　　These are the two extremes: one became a “miracle” without the help of national statistics and macroeconomics—and the future piece will show that by no means was this miracle a “unicorn.” At the same time, the other used macroeconomic jargon, data, and questionable models and failed.

　　How did economists find themselves recommending and perpetuating a practice so friendly to the interests of centralizing governments? This too is a fascinating story. 

　　Tools of Centralization

　　Although institutions around the world made vague calculations of national outputs and incomes for centuries, it was only in June 1932 that the US Senate first required their preparation, appointing the eventual Nobel Prize economist, Simon Kuznets to be in charge. He published two volumes in 1941 titled National Income and Its Composition, 1919-1938. Kuznets and other economists used similar data during WWII when the war effort required centralizing decisions, including rationing, controlling prices, reorienting factories to produce arms instead of consumer products.

　　By the end of the war and in later writings, Kuznets became one of the severest critics of using national accounts and price indices computed during wars to shape policies during decentralized peacetimes. He pointed out that it makes no sense to compare price indices and aggregates during transitions to peacetime numbers, as government drastically diminishes its role in allocating resources. Likewise,countries with dissimilar institutions cannot meaningfully be compared.

　　In 1950, Oskar Morgenstern published the first edition of his “On the Accuracy of Economic Observations,” a detailed analysis of the severe downsides to publishing aggregate data without noting that they are subject to massive errors. In the 1963 edition, he illustrates his point: “It is well known that in France and Italy, income tax returns, [upon which national incomes or personal income distribution are computed], have only a vague resemblance to their actual income patterns” because of their tax rates and regulations.” His and others’ criticism had no effect.

　　In 1987, ISTAT, the Italian government’s statistics office, added 18 percent to the country’s national income. The official argument was that Italy had a large black market due to its bad laws and regulations. In fact, they chose the number 18 because it was the lowest number allowing Italy to comply with the European Community requirement that the country’s deficits relative to its national income must not be greater than its arbitrary 3 percent. The number 18 had nothing to do with estimates of Italy’s black markets, or expecting changes in tax laws to diminish perverse incentives over time . Indeed, Italy did not change its fiscal and regulatory policies, and following the 2008 crisis, found itself in a mess again. Neither Italy nor the rest of Europe took their own aggregate numbers seriously, nor did they initiate policies to bring about sustainably rising standards of living.

　　Consider now Ireland’s 2015 suddenly revised growth rate of 26.3 percent from the projected 7 percent. The new number came about as Ireland lowered its corporate tax rate to 12.5 percent, when in the US the rate was 35 percent. The Irish did not suddenly became brilliant, disciplined workaholics, or entrepreneurs creating many new companies. The new number reflected accounting in “tax inversion deals.” This meant that when US companies acquired a small one and located the headquarters of the new company to Ireland, it resulted in moving large revenues earned around the world to Ireland, and either distribute the increased after-tax income to shareholders or invest more.

　　The tax inversion did not create assets to sustain a much higher standard of living in Ireland. Instead, it quickly brought about steps both in the US and Western Europe to stop tax inversions and prevent losing tax revenues. The new rules in the US made it prohibitively expensive for companies to move headquarters to tax havens. The European OECD (Organization for Economic Cooperation and Development) also came out instantly with its Base Erosion and Profit Sharing to prevent tax competition. Indeed, since 2016, Ireland’s growth went back to being volatile around its pre-2015 range. The aggregate numbers mainly represented an accounting fiction. There was neither asset creation, nor flow of talent to the Irish shores. 

　　The Fed would have been right for decades stating that it just improvises, and has no idea about the magnitude of errors in the aggregate numbers, although thousands of economists work for the Fed and in government.

　　These are just two examples of changes in taxes and regulations which dramatically changed the meaning of aggregates and of price indices.

　　Even when there are less drastic changes, both Kuznets and Morgenstern concluded, “Too much aggregation mixes the unmixable and gives us models that are easy to handle but with low, if any, power of resolution.” Morgenstern then lists a range of errors that John von Neumann likewise noted, concluding that unless we have a clue of the magnitude of errors we expect in the data, “the feeding of economic data into high speed computers is meaningless.” He concluded: “The government should be persuaded to state publicly each time a new gross national product figure is made available that it is known only with an error of say, plus/minus 10%, employment figures with no lesser uncertainty, that foreign trade and balance of payments figures are subject to corresponding doubts.” He further notes, “The fundamental reform that will have to take place is to force the government to stop publishing figures with the pretense that they are free from error. There are no such figures, no matter what the layman may think and no matter what the producers of economics statistics may assert.”

　　It is true that the US Congress formed a Price Statistics Committee in 1959 (the so-called Stigler Committee, headed by George Stigler, another Nobel Prize economist), and the Joint Economic Committee heard the recommendations in 1961. It concluded that the Bureau of Labor Statistics should publish periodically a full description of revisions of methods it used to construct price indices. However, what is the use of such backward-looking information? It cannot help either making better policy or investment decisions.

　　Not surprisingly, in 1996 the so-called “Boskin Commission”again studied price indices which were found wanting. This commission offered a range of recommendations to improve the numbers by finding better ways of measuring changes in quality, technological and medical advances, and other measures. For the most part, bureaus merely put “lipstick on pigs”: They made superficial changes but did nothing fundamentally different, either conceptually or in the calculation or reporting of numbers.

　　Business as Usual?

　　Kuznets, having designed statistical measures for the US War Production Board during World War II, wrote in 1945 that the values of “war and peace type products” could not be compared meaningfully. During wars, military mobilization has priority over consumption, and governments control production and prices.

　　Indeed, by 1943, the Office of Price Administration (OPA) had set up 200 Industry Advisory Committees to control prices. The government banned the production of cars and refrigerators so factories could use steel to make tanks and armaments. It rationed food. The OPA dropped new cars, refrigerators and radios from the consumer-price index, and drastically reduced the weight applied to gasoline. The official numbers showed inflation until 1948 and deflation during 1949 and 1950, yet all knew that the numbers were statistical fictions.

　　A similar farce was repeated during Covid.The government closed down all leisure businesses and schools, and controlled others to fight this “war.” People hardly traveled or went out, while they increased their consumption of delivered food and streamed entertainment. International trade and travel diminished drastically, as in World War II. Strangely, the Bureau of Labor Statistics made no adjustments, even though transportation traditionally accounts for roughly 15 percent of the CPI.

　　Not only did the BLS not adjust its weights to changing patterns of consumption and production, it also announced in 2020 that it was grounding its 400 data-collection specialists. It asked them to find out thousands of prices by phone or Internet and not to survey establishments such as hospitals, physicians’ offices, groceries, and restaurants even by phone—if it would cause an undue burden.

　　Evaluating the 30 percent “housing equivalent” component in the CPI raised even more problems during the Covid years than in normal ones. According to the National Multifamily Housing Council, 69 percent of apartment tenants had paid their monthly rent by April 5, 2020, down from 81 percent the previous month. Thirty-nine states and the federal government imposed eviction moratoriums. Such policies amount to a reduction in housing costs—at least temporarily. I asked BLS how it was dealing with this problem. The answer was that “rent collection is a challenge we are continuing to review and address on an ongoing basis.”

　　What is one to make of price indexes during 2020 and 2021, and of the inflation rate in following years, which the BLS calculated relative to the mismeasured numbers from the pandemic period, and which in any case reflected a conceptually incomparable “wartime” centralized economy, within which prices have an entirely different meaning?

　　The answer is that we must take all of these numbers, like the aggregate numbers after wars, with even more grains of salt than usual. These numbers are thought to be crucial within the present “macroeconomic” frame of mind that the Fed and other central banks use to manage their policies. Although, the Federal Reserve did something right: in August 2020, it announced that since there is no “exact science” for relying on rigid inflation benchmarks to manage their policies, it would, well, improvise. 

　　Unfortunately, nobody followed up and asked: “OK, there is no exact science—but what is this science to start with?” It is important to understand at such a juncture thatall things rooted in aggregates are opinions, not science, even when they wear the mask of science.

　　Hedonic Prices and Academic Opinions

　　In response to criticisms, the BLS and all other statistics bureaus did develop the notion of “hedonic prices.” They estimated the change in, say, the value of computers, by observing that though their prices rose, they were faster and offered more services. Statistics bureaus then use hedonic price indexes drawing on such estimates to deflate a number of GDP final demand components, accounting forabout 20 percent of nominal GDP in recent years in the US. One consequence is that although prices may be rising, the measured “real” GDP becomes higher than in nominal terms.

　　What if (as all the previous observations suggest) there are significant errors, both conceptual and in measurement, when calculating hedonic prices?Consider the following calculations done in 2002. John Kay noted that “real expenditures on computers in 2000 in Britain was about 10 billion pounds. The UK’s Office of National Statistics estimates that computers that cost 10 billion pounds in 2000, would have cost 18 billion pounds in 1995. But if US price indices were used, the figure would be 37 billion pounds.” This number would have meant that Britain’s “real growth” rate would have been much higher.

　　The calculation of hedonic prices is important, but it does not help either with understanding increasing standards of living or with guiding monetary policy. These prices matter very much for businesses because of accounting conventions: accountants and economists must resolve the issue of how much spending on computers and phones they can count as service, as cost against current revenues, and how much they can capitalize. Say, phones, computers, and wireless services are all bundled together, and the company gets a discount by signing a multi-year service contract. Accountants must estimate the subsidies the buyer received for the computer and the phone in exchange for signing the longer-term contract. Such calculations for this one kind of transaction showed that the adjusted price index deflator for Cellular Telephone Services fell at an annual rate of 7.7 percent during 2010–17, 4 percent faster per year than some conventional measures. The difference suggests faster technical advances in such bundled products.

　　Consider the complexity of this one relatively narrow example, and now imagine adding up aggregate numbers across a wide range of final products, not knowing in any category, or combination of categories, the magnitude of errors. Now imagine basing political debates on real GDP, or monetary policy. This makes no sense—and never did.Indeed, the Fed would have been right for decades stating that it just improvises, and has no idea about the magnitude of errors in the aggregate numbers, although thousands of economists work for the Fed and in government (9,300—28 percent out of the total number of 33,245 economists in the US).

　　Hedonic prices or aggregates raise even more serious problems when it comes to measuring standards of living. For, it is not even clear why we should add up the recalculated value of computers when discussing nominal and then adjusting the real GDP. After all, whatever the innovations, their impact was already included in the output of all the companies using them, in their income statement, balance sheet and cash-flow statement too.

　　This last statement brings us easily to the conclusion that if we truly want to know how to raise standards of living, the answer cannot come from any backward-looking aggregates, that nobody knows how to measure with any precision, and what might be the magnitude of errors. If we want better government and improved standards of living, the measurements needed must be forward rather than backward-looking, based on large number of investors’ independent evaluations, derived from companies and governments financial statements, market values of securities—when and where financial markets have depth, allowing assessment of companies and governments access to credit.